Top Data Engineer Jobs in London

Develops and supports business intelligence solutions, including data extraction, data warehouse ETL, dimensional modeling, cubes, reporting, data analysis, and SQL performance tuning. Maintains scheduled processes and BI environments, resolves service issues, supports business reporting needs, and collaborates with underwriting, claims, finance, and management information teams. The role also contributes to data quality, reporting capability, team development, and future BI strategy.

Build governed data assets, reliable pipelines, lakehouse models, and analytical products using Microsoft Fabric and Databricks. Conduct exploratory analysis and develop validated statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, NLP, entity resolution, and information extraction. Implement data quality, lineage, monitoring, testing, documentation, and responsible AI practices while supporting legal-services R&D, client intelligence, opportunity identification, pricing, and matter analysis.

Own electrical engineering and project delivery for energy infrastructure serving data centres and industrial customers. Responsibilities include managing development programmes and budgets, grid connection processes, electrical design, consultant and contractor coordination, design reviews, risk management, procurement, commissioning, energisation and handover. The role requires MV/HV infrastructure experience, GB or European grid connection expertise, multidisciplinary design coordination and accountability across first-of-a-kind power projects.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, catalog registration, reverse ETL, and governance controls. Investigate pipeline failures, perform lineage analysis, review code, identify downstream risks, and support data quality, security, and access controls. Collaborate with stakeholders, clarify requirements, document escalations, and mentor junior data engineers.

Provide first-line data support to quantitative researchers and traders, troubleshoot data access and quality issues with data engineers, manage JIRA workflows, document data processes, onboard and train users, and help expand data coverage through live collection and backfills.

Own the technical vision and architecture for a petabyte-scale data enrichment platform supporting machine-learning deployment, inference, evaluation, monitoring, annotation, and retraining. Build reliable backend services, APIs, workflows, and self-service capabilities across large GPU fleets. Lead cross-team architecture and delivery, establish platform metrics, improve reliability and cost efficiency, and mentor engineers.

Lead the architecture and delivery of scalable batch and real-time data pipelines on GCP. Own data modeling, security, governance, reliability, performance, cost optimization, monitoring, and critical incident resolution. Translate complex business needs into robust data solutions, evaluate AI and data-engineering technologies, mentor engineers, and influence technical standards across teams.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

Design, build, and optimise cloud-based data pipelines, ETL/ELT processes and enterprise data platforms for an insurance client. Integrate multiple source systems, ensure data quality and performance, support data modelling and transformation, and collaborate with stakeholders using Agile practices.

Designs and operates scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, product, and business teams while contributing to standards, testing, documentation, code reviews, production support, and incident response.

Design, develop and automate data pipelines ingesting transactional, social and geographic data. Build crawlers and APIs, develop algorithms and statistical models (including clustering) for large-scale graph data, and improve infrastructure and internal tools.

Design, build, and maintain scalable ETL/data pipelines and storage to support analytics and ML across international markets. Ensure data quality, compliance (e.g., GDPR), monitoring, and automation. Collaborate with data scientists, product teams, and stakeholders to deliver reliable datasets and contribute to the data infrastructure roadmap. Onsite in London.

Leads the technical direction of data engineering, owning scalable pipelines, warehouse architecture, data models, ELT transformations, quality monitoring, and platform reliability. Defines engineering standards, reviews designs and code, drives CI/CD and observability, resolves complex technical issues, and mentors engineers. Collaborates with analytics, reporting, data science, and platform teams to translate requirements into performant, cost-effective solutions while contributing to warehouse modernization and technical planning.

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Design, build, and maintain large-scale, secure data engineering solutions. Lead architecture, deliver production code, drive automation and operational stability, mentor teams, and evaluate vendor/technology choices to advance risk technology platforms.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Design, build, and operate cloud-native lakehouse and streaming data platforms and pipelines using Python, PySpark, dbt, Airflow, and orchestration/infrastructure tooling. Ensure performance, reliability, security, and observability; model and transform data for analytics; deploy with IaC, containers, and Kubernetes; mentor engineers and drive platform standards and best practices.

Lead data migration strategies and implementation across hybrid cloud environments, design data pipelines, and optimize scalable systems while collaborating on AI projects.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Build and operate high-volume ingestion pipelines for procurement and supply-chain data. Design schemas, catalogs, warehouses, lakehouses, and feature stores; establish data quality, monitoring, observability, governance, and lineage standards. Collaborate with AI, Platform, and Product teams to provision reliable datasets for analytics and machine learning. Own end-to-end data solutions, CI/CD, integrations, and architecture supporting enterprise customers.